The pharmacological mechanism of β-elemene in the treatment of esophageal cancer revealed by network pharmacology and experimental verification

The study aimed to investigate the mechanism of action of β-elemene (ELE) in the treatment of esophageal cancer (EC). In this study, public databases were used to predict related targets in ELE and EC. The network analysis was performed to identify key targets of ELE in EC treatment. Further, bioinformatics and DAVID databases were used for GO and KEGG enrichment analysis, respectively. Ultimately, molecular docking and in vitro cell experiments were conducted to validate the results of network pharmacology enrichment. As a result, 34 candidate targets for ELE in the treatment of EC were obtained, and five key targets (STAT3, EGFR, CTNNB1, BCL2L1 and CASP9) were identified. GO functional annotation yielded 2200 GO entries (p < 0.05). KEGG signaling pathway enrichment analysis screened 100 pathways (p < 0.05). Molecular docking results showed that ELE had similar affinity with five key targets. In vitro experiments showed that the expressions of STAT3, EGFR and BCL2L1 were significantly decreased, and the expression of CASP9 in the ELE intervention group was significantly increased compared with that in the control group. All in all, ELE may play a key role in the treatment of EC by regulating the expression of STAT3, EGFR, BCL2L1 and CASP9.


Results
Candidate targets of ELE in the treatment of EC. The numbers of ELE targets obtained from the TCMSP, CTD, Pubchem and SwissTargetPrediction databases were 25, 11, 17 and 78, respectively. Taking the union of the ELE targets in the above four databases, the final number of ELE targets was 116. The results are presented in Fig. 2A. The EC targets obtained from the NCBI, GeneCards and OMIM databases were 636, 6013 and 195, respectively. The targets of NCBI and GeneCards databases were intersected, and then merged with the targets of OMIM database, and finally, 796 targets of EC were obtained. The results are shown in Fig. 2B. Taking the intersection of the targets of ELE with the targets of EC, 34 candidate targets of ELE for the treatment of EC were finally obtained. The results are exhibited in Fig. 2C.  www.nature.com/scientificreports/ PPI network and prediction of key targets. The PPI network was constructed using the STRING database and visualized by Cytoscape software. As a result, the PPI network contained 34 nodes and 336 edges (Fig. 3). Then, the betweenness centrality (BC), closeness centrality (CC), and degree centrality (DC) of the target were calculated using topological analysis. The top ten center nodes of ELE in the treatment of EC are shown in Table 1

Effects of ELE on mRNA expression levels of key targets in ECA-109 cells.
In order to preliminarily study the mechanism of action of ELE in the treatment of EC, we treated ECA-109 cells with different concentrations of ELE for 24 h, and then detected mRNA expression levels of key targets by RT-qPCR. As shown in Fig. 7B-F, compared with the control group, in the ELE intervention group, the mRNA expression levels of STAT3, EGFR and BCL2L1 were significantly decreased; the mRNA expression of CASP9 was significantly increased; and the expression of CTNNB1 was not significantly different.

Effects of ELE on protein expression levels of key targets in ECA-109 cells.
To further clarify the effect of ELE on the expression of key targets in ECA-109 cells, STAT3, EGFR, BCL2L1 and CASP9, which were statistically significant with regard to the mRNA expression levels between the two groups, were selected for further study, and western blot was used to detect the protein expression levels of STAT3, EGFR, BCL2L1 and CASP9 after ELE intervention. As shown in Fig. 8, the results showed that the protein expression levels of STAT3, EGFR and BCL2L1 in the ELE intervention group were significantly reduced, while the protein expression levels of CASP9 were significantly increased compared with the control group.

Discussion
This study investigated the mechanism of action of ELE in EC treatment via network pharmacology analysis and experimental validation. As a result, we identified five key candidate targets for ELE in the treatment of EC, namely STAT3, EGFR, CTNNB1, BCL2L1 and CASP9. Then, in vitro cell experiments confirmed that ELE may play a certain role in the treatment of EC by down-regulating the expression levels of STAT3, EGFR and BCL2L1 and up-regulating CASP9 expression. The theory of TCM holds that human body is an organic whole, and the treatment of diseases needs to be comprehensively considered from multiple angles and aspects, in order to better play the role of drugs in symptomatic treatment and delaying the development of diseases. Network pharmacology is a brand-new discipline that integrates systems biology and bioinformatics. It focuses on systematically excavating the mechanism of drug treatment of diseases as a whole, which is consistent with the concept of TCM for disease treatment 18 . Ancient physicians believed that the etiology and pathogenesis of EC were deficiency, blood stasis, heat, and toxin, and the treatment should be used to invigorate Qi and promote blood circulation, strengthen spleen and kidney, regulate Qi and resolve phlegm, clear heat and detoxify 19 . The TCM Curcuma wenyujin has the effects of promoting Qi and relieving depression, promoting blood circulation and relieving pain, and promoting gallbladder and removing jaundice, and is widely used in the clinical treatment of lung cancer, liver cancer, breast cancer, pancreatic cancer, EC, etc. Beta-elemene, as the active compound extracted from Curcuma wenyujin, plays a key role in the treatment of various cancers including esophageal cancer, but the mechanism of action remains to be further studied 20 . Therefore, this study explored the mechanism of action of ELE in the treatment of EC based on network pharmacology, which may provide a new method for the treatment of EC.
To explore the key targets of ELE in the treatment of EC, this study integrating PPI network analysis with literature search revealed that STAT3, EGFR, CTNNB1, BCL2L1 and CASP9 may be core targets. Among them, the core target with a higher degree value was STAT3. Research has shown that STAT3 is a very important transcription factor of the STAT family, which is thought to be involved in the regulation of various key functions, including cell survival, differentiation, apoptosis and metastasis, and is overexpressed in multiple cancer cells 21 . Besides, studies have also found that inhibiting the expression of STAT3 can induce apoptosis and G1 cell cycle arrest in EC ECA109 cells, and inhibit cell migration 22 . Our result also found that ELE intervention was able to significantly reduce STAT3 expression in ECA-109 cells, which further suggested that STAT3 may be a key target of ELE for the treatment of EC.
Besides, EGFR, BCL2L1 and CASP9 also have high degree values in the PPI network. EGFR, a tyrosine kinase receptor in the ErbB family, is involved in various processes of cancer progression, including cell proliferation, migration and metastasis 23,24 . It has been reported that EGFR is highly expressed in a variety of cancers, such as lung cancer, breast cancer, colorectal cancer 25 . It is worth noting that the increased expression of EGFR in EC is closely related to cancer metastasis and poor prognosis 26 . BCL2L1, also known as BCL-xl, is one of the BCL-X subtypes belonging to the BCl2 family, and the other subtype is BCL-xs. Interestingly, the two have opposite effects, with BCL-xl being anti-apoptotic and BCL-xs being pro-apoptotic 27 . Currently, BCL2L1 has been found to be highly expressed in a large number of cancers, including myeloma, lymphoma, liver cancer, gastric cancer, and ovarian cancer 28 . CASP9, a cysteine-aspartic protease, plays a crucial role in regulating cell differentiation, proliferation, and apoptosis, and is associated with the onset and progression of various cancers 29 . One study has showed that Tanshinone IIA could significantly induce apoptosis and inhibit the proliferation of human EC Eca-109 cells in vitro by up-regulating the expression of CASP9 30 . Taken together, these data further support our view of the importance of ELE in the treatment of EC.
In conclusion, through network pharmacology and validation experiments, we have revealed that ELE may play a role in the treatment of EC by regulating the expression of STAT3, EGFR, BCL2L1, and CASP9. It should be noted that this study also has some limitations, namely, this study uses a data mining approach to explore the possible mechanisms of ELE in the treatment of EC, but further in vivo animal experiments are needed to confirm the findings. Predicting EC targets. Targets related to EC were obtained from the NCBI (https:// www. ncbi. nlm. nih. gov/), GeneCards (https:// www. genec ards. org/) and OMIM (https:// www. omim. org/) databases.
Construction of PPI networks. The candidate target list of ELE in the treatment of EC was input to the STRING database (https:// string-db. org/), and the species was selected as "Homo sapiens" to construct a PPI network. The "string_interactions file" was downloaded from the PPI network analysis results of the STRING database, and the visualization and topology analysis of the PPI network were then performed using Cytoscape 3.9.1 software.
GO functional annotation and KEGG pathway enrichment analysis. The candidate target list of ELE in the treatment of EC was uploaded to the bioinformatics platform (http:// www. bioin forma tics. com. cn/), and Gene Ontology (GO) functional annotation was performed using the GO enrichment analysis function of the bioinformatics platform. Similarly, the candidate target list of ELE for the treatment of EC was uploaded to the file selection column of the DAVID database (https:// david. ncifc rf. gov/) for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

Construction of ELE-EC-target-pathway network.
The one-to-one correspondence of ELE-EC-target-signaling pathway was sorted out in EXCEL, and the network of ELE-EC-target-signaling pathway was constructed using Cytoscape 3.9.1. Among them, the top 20 signaling pathways ranked by p value were incorporated into the ELE-EC-target-signaling pathway network.  Subsequently, a total of 30 μg of proteins were separated by SDS-PAGE on 10% gels, which were then transferred onto PVDF membranes and blocked with 5% skim milk for 2 h at room temperature. The PVDF membranes were incubated with the corresponding primary antibodies (STAT3, EGFR, BCL2L1 and CASP9 (ZEN BIO, China); GAPDH (Proteintech, USA)) overnight at 4 °C. After washing with TBST, the PVDF membranes were incubated with secondary antibody overnight at room temperature. Finally, detection was routinely performed with a chemiluminescent HRP substrate (Beyotime, Shanghai, China) and an ECL imaging system (Tanon, Shanghai, China). The result of gels images was cropped and full-length gels and blots are included in the Supplementary Data.
Statistical analysis. SPSS 22.0 was used for statistical analysis of experimental data. Data were presented as mean ± standard deviation (SD), and differences between multiple groups were assessed using one-way analysis of variance (ANOVA). A p-value < 0.05 was considered statistically significant.

Conclusion
In summary, the present study is the first to systematically explore the possible mechanisms of ELE in the treatment of EC using network pharmacology approach and in vitro validation experiments. The network pharmacological analysis predicted that ELE may exert its therapeutic effects against EC via regulating multiple targets and pathways. In vitro validation experiments confirmed that the possible mechanism of ELE treating EC was to down-regulating the expression of STAT3, EGFR and BCL2L1, and up-regulating the expression of CASP9, thereby improving the progression of EC. This finding may serve as a reference to study the mechanism of action of ELE in the treatment of EC and to supply novel targets for the treatment of EC.

Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.